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Docs Prettier reformat (#13483)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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6
docs/en/guides/kfold-cross-validation.md
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@ -29,7 +29,7 @@ Without further ado, let's dive in!
- It includes 6 class labels, each with its total instance counts listed below.
| Class Label | Instance Count |
|:------------|:--------------:|
| :---------- | :------------: |
| Apple | 7049 |
| Grapes | 7202 |
| Pineapple | 1613 |
@ -173,7 +173,7 @@ The rows index the label files, each corresponding to an image in your dataset,
fold_lbl_distrb.loc[f"split_{n}"] = ratio
```
The ideal scenario is for all class ratios to be reasonably similar for each split and across classes. This, however, will be subject to the specifics of your dataset.
The ideal scenario is for all class ratios to be reasonably similar for each split and across classes. This, however, will be subject to the specifics of your dataset.
4. Next, we create the directories and dataset YAML files for each split.
@ -219,7 +219,7 @@ The rows index the label files, each corresponding to an image in your dataset,
5. Lastly, copy images and labels into the respective directory ('train' or 'val') for each split.
- __NOTE:__ The time required for this portion of the code will vary based on the size of your dataset and your system hardware.
- **NOTE:** The time required for this portion of the code will vary based on the size of your dataset and your system hardware.
```python
for image, label in zip(images, labels):